In the past, determining the price of data processing services has been relatively straightforward. For example, in the mainframe environment, prices have been based on the capability of equipment as measured by the execution of millions of instructions per second (MIPS); in the desktop environment, prices have been set according to the number of “seats” supported, i.e., according to the maximum number of users permitted to access a particular program or service simultaneously.
Neither of these measures, however, is entirely satisfactory in today's emerging mid-range environment where system integrators provide turn-key data processing services that may involve computer equipment, servers, applications, and communications. Such data processing services are now laboriously priced, offering by offering, according to the specific technologies and platforms involved.
Unfortunately, when the customer's needs change, the price must be laboriously adjusted, based on changes in the technologies and platforms that underlie the data processing services. Not only are price updates laborious and therefore costly, they are also time consuming, and introduce delays that seem to make the system integrator unresponsive to “what if ?” questions posed by customers. Further, customers are unable to see immediate relationships between changes in services and changes in prices, and instead often perceive only the anomalies introduced by technology shifts. Thus, today price is linked disadvantageously with technology rather than linked with the actual service provided by the system integrator. Such a link of service price to technology will ultimately fail when technology changes.
Thus there is a need for an improved way of pricing data processing services provided by system integrators, so that changes to baseline prices may be made quickly, inexpensively, and accurately, independently of technology changes, so that customers of system integrators are able to see relationships between changes in services and changes in prices.